Peer Aid
A peer-to-peer anonymous support network for student mental well-being.
Project Overview
PeerAid is a peer-to-peer, privacy-first platform connecting people with specific health conditions to verified guides who have real treatment experience. Users create structured health profiles, discover compatible peers, and communicate via encrypted chat and peer-to-peer audio calls—all while remaining pseudonymous. The platform is designed to foster trust, safety, and transparency in sensitive health interactions.
Why I Built This
Many people with similar health conditions rely on social media for advices and counsellings, which is often unverified, unstructured, and unsafe. Existing platforms either expose too much personal information or provide shallow matching, making it difficult to find reliable, relevant guidance while protecting privacy. PeerAid addresses this gap with a trust- and privacy-centered approach.
What It Solves
Centralizes structured peer experiences tailored to each user’s profile.
Finds compatible guides based on conditions, symptoms, demographics, and verified treatments.
Provides transparent matching explanations, verification signals, and consent-driven workflows.
Enables pseudonymous interactions and secure sharing of medical documents.
System Architecture
Computes weighted compatibility scores and explains each match.
Multi-step profile capture with optional medical document verification.
Anonymous, encrypted chat and peer-to-peer WebRTC audio calls with explicit consent.
Connection requests, accept/reject flows, persistent conversations, and system messages.
REST API for persistence, WebSocket server for chat and signaling, service layer for reusable business logic.
Cloudinary manages uploads with strict access control, separating verification and shared content.
Learnings
This project reinforced that trust is multi-layered: transparency, verification, and consent are as important as the core feature. I learned how to design real-time systems with predictable state and lifecycle handling, how to separate identity verification from exposure to maintain privacy, and how to build workflows that handle sensitive data safely while keeping the experience seamless for users.